The present invention relates to a probabilistic, abductive system and method for recognizing goals or plans of an actor or agent. More particularly, it relates to a system and method that utilizes observed activities in conjunction with hypothesized unobserved activities to probabilistically recognize a goal/plan of an actor or agent. Application domains include automated monitoring and response systems for daily living environments, computer network security, and competitive analysis, to name but a few.
The concept of automatically recognizing or predicting a goal or intent of an actor or agent based upon tracked events has been the subject of intensive research efforts. Countless domain applications could benefit from such a system or model. Unfortunately, the complexities of these domains have heretofore prevented formulation of a truly useful intent recognition system or model. As a point of reference, the terms “actor” and “agent” are used interchangeably throughout this specification, and relate to human and non-human subjects.
One potential application for an intent recognition device relates to a human actor monitoring and support system. In this regard, the evolution of technology has given rise to numerous, discrete devices adapted to make daily, in-home living more convenient. For example, companies are selling microwaves that connect to the internet, and refrigerators with computer displays, to name but a few. These and other advancements have prompted research into the feasibility of a universal home control system that not only automates operation of various devices or appliances within the home, but also monitors activities of an actor in the home. Such a system could further perform device control based upon the actor's activities and/or events in the living space.
In general terms, such an automated human actor monitoring and response system will consist of a suite of sensors in the living space to detect actor actions and environment states, a set of actuators that control devices in the environment as well as facilitate communication with the actor (and others), and a computer system (local or remote) capable of making decisions to assist the actor. With respect to this “assistance” feature, a necessary attribute resides in not only understanding what actions or plans the actor has already completed, but also inferring goals of the actor, or more simply, “what is the actor trying to do”. By knowing the actor's intended goals or plans, the system would be able to “pre-emptively” respond. For example, with intent or goal inference capabilities, the monitoring and response system could lock a door before a demented actor attempted to leave his/her home, provide next step-type suggestions to an actor experiencing difficulties with a particular activity or task, suppress certain warning alarms in response to a minor kitchen fire upon recognizing that the actor is quickly moving toward the kitchen, etc.
Artificial intelligence research has produced preliminary models for achieving plan recognition of an actor or agent that could be potentially applied to one or more domains of interest. For example, Kautz, H. and Allen, J. F., “Generalized Plan Recognition,” Proceedings of the Fifth National Conference on Artificial Intelligence, pp. 32–38 (1986) defined the problem of plan recognition as the problem of identifying a minimal set of top-level actions sufficient to explain the set of observed actions. To a first approximation, in their formulation the problem of plan or intent recognition is viewed as a problem of graph covering.
Others, such as Chamiak, E. and Goldman, R. P., “A Bayesian Model of Plan Recognition,” Artificial Intelligence, 64 (1); 53–79 (1993), have recognized that since plan recognition involves abduction, it could best be done as a Bayesian inference. Bayesian inference supports the preference for minimal explanations, in the case of hypothesis that are equally likely, but also correctly handles explanations of the same complexity but different likelihoods.
Further probabilistic-based efforts have addressed the problems of influences from the state of the world and evidence from failure to observe. For example, Goldman, R. P., Geib, C., and Miller, C., “A New Model of Plan Recognition,” Proceedings of the 1999 Conference on Uncertainty in Artificial Intelligence, Stockholm, (July 1999), advance an abductive, probabilistic theory of plan recognition that accounts for the accumulative effect of a sequence of observations of partially-ordered, interleaved plans and the effect of context on plan adoption.
While probabilistic plan recognition is a preferred method for inferring the goals of an agent, as it results in a measure of the likelihood for each hypothesized plan, the previously-advanced techniques fail to account for unobserved actions. That is to say, existing work on plan recognition has assumed complete observability of the agent's actions. However, implementation of a probabilistic plan recognition system into actual working environments, such as part of an in-home monitoring and response system, must account for the fact that complete observability of all actions is virtually impossible to achieve.
For example, relative to the exemplary in-home monitoring and support domain, sensors will inevitably fail to detect an action or event (e.g., due to sensor malfunction). Requiring that this “missed” action or event occur (or be “sensed”) before the model “recognizes” a particular goal will overly limit the overall usefulness of the system. Unfortunately, the ability to consider that unobserved actions have occurred, as part of an overall probabilistic evaluation is inherently problematic, as consideration of potentially unobserved actions results in a significant expansion of the problem's search space.
In addition to an in-home actor monitoring and support system, a number of other domains would clearly benefit from an efficacious intent recognition system or model. For example, a computer network security system capable of recognizing the intended goals of a hacker, and then taking necessary steps to stop the hacker's efforts would be highly advantageous. In most, if not all, of these potential domains, truly effective intent recognition requires that unobserved actions be accounted for. Unfortunately, this feature has not been fully perfected with current methodologies. Therefore, a need exists for a system and method of probabilistically recognizing a goal of an actor or agent based upon the inferred presence of unobserved actions, with the system and method being applicable in various domains such as in conjunction with an in-home monitoring and response system.